Integrative Molecular Characterization of Resistance to Neoadjuvant Chemoradiation in Rectal Cancer

This article is featured in Highlights of This Issue, p. 5429

Radiotherapy is used in the management of nearly two-thirds of cancers (1), often fulfilling the role of a curative treatment modality in place of surgery. Therapeutic radiation can be adapted to target tumors in various anatomic locations as well as various malignancies. The radiation dose and fractionation can be altered to maximize tumor killing while sparing normal tissues (2). Radiotherapy is typically combined with concurrent chemotherapy (CRT) in locally advanced disease. When used neoadjuvantly, pathologic downstaging is a surrogate of long-term outcome in many disease sites (3–7). For example, in rectal cancer, approximately 9%–20% of patients with locally advanced disease have a pathologic complete response (pCR) to neoadjuvant CRT (8), while 20%–40% of patients have little to no response (9, 10). Predictive biomarkers of pCR remain to be established.

The major mechanism of radiation-induced cell killing is likely through DNA damage. There is, however, emerging evidence that radiation also has effects on the tumor microenvironment with variation based on anatomic site, tumor histology, and multiple other characteristics (11). These cell killing effects can be further augmented by combining radiation with radiosensitizing systemic agents (12). In addition, there is recent interest in the utility of radiation to alter the adaptive immune response to improve treatment outcomes by creating a local antitumor immune response that may be modulated into a systemic antitumor immune response with the use of immunomodulatory agents (13–15). Proposed mechanisms include possible creation of increased neoantigens or tumor mutational burden (TMB) through the DNA-damaging effects of radiation (16, 17), the latter of which has been previously demonstrated to correlate with response after treatment with immune checkpoint inhibitors (18, 19). Despite the widespread use of radiotherapy for solid tumors, there has been slow progress in predicting treatment outcomes to radiation to allow for personalization of therapy on an individual level (12, 15). Biomarkers have been in use and ultimately transformed the field of systemic therapy, while few predictive biomarkers are available for radiotherapy (12).

Using rectal cancer as our model (3, 20), we hypothesized that a comprehensive assessment of patient-matched pre- and post-CRT specimens, examining both tumor-intrinsic and microenvironmental features from the tumor site, may reveal features associated with treatment response at the molecular level. To that end, we leveraged a cohort of patients with locally advanced rectal cancer who underwent fluoropyrimidine-based CRT to a dose of 50.4 Gy followed by surgical resection and analyzed genomic tumor changes in the matched pre- and posttreatment rectal tumor samples to identify drivers of resistance to neoadjuvant CRT and thereby identify biomarkers for patient stratification.

We retrospectively identified patients with biopsy-proven locally advanced rectal cancer (defined as T3–4 or N+) who received neoadjuvant fluoropyrimidine-based chemotherapy concurrently with 50.4 Gy radiotherapy, followed by surgical resection within 8–11 weeks between 2010 and 2016 (20). Patients then went on to receive adjuvant systemic therapy, which consisted of FOLFOX (21). Patients had to have documented written consent through the institutional review board–approved protocol that collects tissue and whole-blood specimens on patients with gastrointestinal malignancies in accordance with the Declaration of Helsinki and all applicable legal regulatory requirements. There were 77 patients who met initial criteria. Eligible patients had to have sufficient tumor tissue in study specimens of formalin-fixed, paraffin-embedded (FFPE) tissue sections from surgical samples, as well as a germline DNA specimen that was extracted from either peripheral mononuclear cells or histologically normal rectal tissue. Twenty patients were identified with sufficient tissue available. All patients were arbitrarily identified with no prior knowledge of genomic tumor status. All samples had to pass standard quality control measures. We identified 34 pre- and post-CRT–matched tumor samples from 17 patients in our final cohort. Nine and 8 patients were classified as nonresponders (no evidence of any pathologic downstaging, NR) and responders (pathologic complete response or pathologic partial response, R), respectively, at surgery based on pathologic evaluation.

DNA extraction, whole-exome library preparation, and sequencing were performed for the samples as described previously (22, 23). Slides were cut from FFPE blocks and examined by a board-certified pathologist to select high-density cancer blocks and ensure high purity of cancer DNA. Biopsy cores were taken from the corresponding tissue block for DNA extraction. DNA was extracted using Qiagen's QIAamp DNA FFPE Tissue Kit Quantitation Reagent (Invitrogen). DNA was stored at −20°C.

Whole-exome capture libraries were constructed from 100 ng of DNA from tumor and normal tissue after sample shearing, end repair, and phosphorylation and ligation to barcoded sequencing adaptors. Ligated DNA was size selected for lengths between 200 and 350 bp and subjected to exonic hybrid capture using The Broad Institute Genomics Platform Custom Illumina bait. The Illumina exome specifically targets approximately 37.7 Mb of mainly exonic territory made up of all targets from the Agilent exome design (Agilent SureSelect All Exon V2), all coding regions of Gencode V11 genes, and all coding regions of RefSeq gene and KnownGene tracks from the UCSC genome browser (http://genome.ucsc.edu). The sample was multiplexed and sequenced using Illumina HiSeq technology.

Sequencing was performed to an average depth of 150 ×. Data were analyzed using the Broad Picard Pipeline, which includes demultiplexing and data aggregation.

Initial data processing and analysis of exome sequence data were performed using Broad Institute pipelines as described previously (23). Using the Broad Picard Pipeline for alignment, BAM files were uploaded into the Firehose infrastructure to manage intermediate analysis files executed by analysis pipelines. Quality control modules in Firehose (24) were run to compare the tumor and normal genotypes and ensure concordance between samples. Of samples from 20 initial patients, six samples from 3 patients were abandoned because of high estimates of tumor contamination (25), inadequate coverage (<40× tumor average coverage), or low tumor purity (26). This yielded a final number of 17 total pairs of pre- and posttreatment tumors for analysis.

The MuTect algorithm (27) was applied to identify somatic single-nucleotide variants in targeted exons. Strelka (28) was used to identify small deletions or insertions, and alterations were annotated with Oncotator (29). Mutations were examined for distribution and type and confirmed using the integrative genomics viewer (30, 31).

Using established protocols (32), total RNA was assessed for quality using the Caliper LabChip GX2. The percentage of fragments with a size greater than 200 nucleotides (DV200) was calculated using software. An aliquot of 200 ng of RNA was used as the input for first strand cDNA synthesis using Illumina's TruSeq RNA Access Library Prep Kit. Synthesis of the second strand of cDNA was followed by indexed adapter ligation. Subsequent PCR amplification enriched for adapted fragments. The amplified libraries were quantified using an automated PicoGreen assay.

A total of 200 ng of each cDNA library, not including controls, were combined into 4-plex pools. Capture probes that target the exome were added and hybridized for recommended time. Following hybridization, streptavidin magnetic beads were used to capture the library-bound probes from the previous step. Two wash steps effectively remove any nonspecifically bound products. These same hybridization, capture, and wash steps are repeated to assure high specificity. A second round of amplification enriches the captured libraries. After enrichment the libraries were quantified with qPCR using the KAPA Library Quantification Kit for Illumina Sequencing Platforms and then pooled equimolarly. The entire process is in 96-well format and all pipetting is done by either Agilent Bravo or Hamilton Starlet.

Pooled libraries were normalized to 2 nmol/L and denatured using 0.1 N NaOH prior to sequencing. Flowcell cluster amplification and sequencing were performed according to the manufacturer's protocols using HiSeq 2500. Each run was a 76 bp paired-end with an eight-base index barcode read. Data was analyzed using the Broad Picard Pipeline, which includes demultiplexing and data aggregation.

HLA-type was inferred using POLYSOLVER (33) which uses a normal tissue BAM file as input. It then employs a Bayesian classifier to determine the genotype for each patient. Neoantigens were predicted for each patient by defining all novel amino acid 9mers and 10mers resulting from mutations (23). We filtered out mutations with <3 supportive reads, or <30 total reads at the position. Neoantigen prediction continued on the basis of whether predicted binding affinity to the patient's germline HLA alleles was <500 nmol/L using NetMHCpan (34). Correlations and associated P values between neoantigen load and R versus NR was performed using Mann–Whitney U tests, P < 0.05 was considered significant.

Purity and ploidy for each sample was estimated using ABSOLUTE algorithm (35). This algorithm integrates variant allele frequency distributions and copy number variants to estimate absolute tumor purity and ploidy and infer cancer cell fraction (CCF), which is the proportion of cancer cells in the sample which contain each mutation. An ABSOLUTE extension algorithm (35) was used to construct an inferred phylogenetic tree with clones, subclones, and evolutionary relationships in pre- and posttreatment tumor samples. As described by Brastianos and colleagues (36), clones and subclones were determined through Markov Chain Monte Carlo sampling using Dirichlet process Mixture Models on pre- and post-CRT mutation CCFs, which assigns mutations to subclones without prespecifying the number of subclones. Mutations inferred to be in a subclone with a CCF ≥ 0.8 were described as “clonal,” while those inferred to be in a subclone with CCF < 0.8 were called “subclonal.” For each subclone, two CCFs were inferred; one CCF in the pretreatment tumor and CCF in the posttreatment tumor (23).

Changes in mutational, neoantigen, and indel load were calculated using a paired t test of changes in paired samples with a null hypothesis of a difference of 0 (23). P < 0.05 was considered to be statistically significant.

We used MutSig2CV (26) to identify significantly mutated genes across our cohort of pre-CRT and post-CRT tumors. Each altered gene in the pretreatment tumors had a P value calculated for mutational significance considering only mutations private to these samples. Similarly, a P value of mutational significance considering only those mutations private to the posttreatment tumor was calculated. Adjustment for hypothesis testing was performed using a Benjamini–Hochberg FDR of 0.1 (23).

Available RNA-Seq data were analyzed as described previously (37). Briefly, expression data were examined and adjusted for batch effects using ComBat (38) using the R Bioconductor package “sva” V3.8 (39). Gene set enrichment analysis (40) was run using https://genepattern.broadinstitute.org using 50 “Hallmark” gene sets to investigate differences in gene set expression in R versus NR (pre-CRT R vs. pre-CRT NR; post-CRT R vs. post-CRT NR) with 1,000 permutations, type “gene_set.” Gene level transcripts per million (TPM) were the input. Family-wise error rates were calculated to identify significant gene sets.

To determine the relationship between CRT and the immune landscape, we analyzed matched transcriptomes from the tumors using CIBERSORT (41) to deconvolute immune cell populations from bulk transcriptome data using immune cell–associated signatures. From this, we inferred overall immune infiltrate and relative immune cell populations in both the pre-CRT and post-CRT specimens. This was run using the CIBERSORT interface (https://cibersort.stanford.edu). The analysis was set to absolute quantification output. Input was gene level TPM and leukocyte gene signature matrix (LM22; ref. 41) was used to deconvolve 22 immune cell subset populations. Absolute quantification normalizing by the 50th percentile of overall gene expression generated a metric that is comparable between samples. Correlations and associated P values between groups of pre-CRT versus post-CRT and R versus NR was performed using Mann–Whitney U tests, P < 0.05 was considered significant. To account for multiple hypothesis testing, a Benjamini–Hochberg FDR of 0.1 was used to identify highly significant associations.

Details of the six antibodies (PD1, PD-L1, PD-L2, CTLA4, CD4, and CD8), host species, clone, and dilatation are given in Supplementary Table S1. IHC was performed automatically using a Benchmark XT/Discovery ULTRA Staining Module (Ventana Medical Systems, Inc.) using established protocols (42). In brief, protocols consisted of pretreatment with CC1 (pH 8.0), incubation with primary antibodies, and detection using a DAB-system (catalog no. 760-500, Ventana Medical Systems, Inc) including ultraview inhibitor, horseradish peroxidase, multimer chromogen, H₂O₂, and copper. In brief, sections were washed for 5 minutes (xylene × 3, 100% ethanol × 2, 95% ethanol × 1, 70% ethanol × 1, and PBS × 1). Staining properties and specificity have been determined previously (refs. 37, 43–47; Supplementary Table S1), which we additionally ascertained using negative and positive controls (Tonsil).

For light microscopy, we captured images using an Olympus DP27 camera attached to an Olympus BX40 light microscope (Olympus America). All markers were evaluated on tumor and nontumor compartments and scored as positive versus negative using established cut-off points (48–50). For CD4 and CD8 we additionally captured four images (high power field, 400×) and applied established image quantification tools. Briefly, segmentation of cells was achieved using threshold filters in combination with circularity and size cutoffs using “cell counter” and “analyze particle” plug-ins in ImageJ Software (NIH, Bethesda, MD; ref. 42). For statistical analysis of CD4 and CD8 staining of immune infiltrates, we took the average and median of four independent regions of interest. Differences in CD4 and CD8 T-cell infiltrates between pre-/post-CRT samples were calculated using a t test of changes with a null hypothesis of a difference of 0. P < 0.05 was considered to be statistically significant. Correlations and associated P values between groups of pre-CRT versus post-CRT, R versus NR, and KP genotype versus no KP genotype were performed using Mann–Whitney U tests, P < 0.05 was considered significant.

We analyzed the association between R versus NR and KRAS/TP53 mutation genotype versus no KRAS/TP53 mutation genotype with progression-free survival (PFS) using the Kaplan–Meier method. All statistical tests were performed using R version 3.5.2 and Prism 8 Software (GraphPad).

All BAMS for the matched pre- and posttreatment tumors will be deposited in dbGAP (phs001829.v1.p1).

We assembled a cohort of 17 patients with locally advanced rectal carcinoma, of whom 9 were characterized pathologically as R and 8 as NR following neoadjuvant CRT (Materials and Methods). Tumor genotype was unknown at the time of case identification. These patients had sufficient pre-CRT biopsy tissue and post-CRT surgical resection tissue available for multiple analytical pipelines including deep whole-exome sequencing (Fig. 1A). Demographic, treatment, and tumor characteristics are summarized in Supplementary Tables S2 and S3. All tumors demonstrated microsatellite stability. Median follow-up of the cohort was 47.1 months (range, 5.8–90.6). There were no local tumor failures. Overall, NR status was associated with reduced PFS compared with R with 5-year PFS 44% versus 100%, respectively (log-rank P = 0.02; Fig. 1B). Median PFS for NR and R was 24.8 months and not reached, respectively.

No statistically significant change in TMB before and after exposure to CRT was observed in our cohort (P = 0.40; Fig. 1C). A similar analysis of predicted neoantigen burden between pre- and post-CRT tumors also demonstrated no statistically significant change (P = 0.12; Fig. 1D). Neither pre- nor post-CRT neoantigen load were associated with treatment response (P = 0.81; Supplementary Fig. S1 and P = 0.42; Supplementary Fig. S2, respectively). We also found no difference in indel loads between pre- and posttreatment samples (P = 0.20; Supplementary Figure S3). As has been demonstrated previously (51–54), the most frequently mutated genes pre- and post-CRT included KRAS, TP53, and APC (Fig. 1E). Thus, global somatic mutations were not impacted by exposure to CRT in this cohort.

In evaluating differences in specific somatic mutations between R versus NR cases, we observed that NR tumors were enriched for concurrent KRAS and TP53 mutations (KP genotype) in contrast to R tumors (Fisher exact P = 0.05; Fig. 2A and B), as has been described previously (55–57). Notably, one pre-CRT KRAS-mutated tumor harbored a TP53 mutation post-CRT that was not detected in the pretreatment tumor despite sufficient power to detect a mutation; this patient was also a NR (Fig. 2C; Supplementary Figs. S4 and S5), suggesting emergence of a radioresistant subclone. Given its association with NR, we next investigated the association between KP genotype and PFS. Patients with the KP genotype experienced reduced 5-year PFS (38%) compared with those without (90%, log-rank P = 0.04; Fig. 2D).

To complement our investigation of tumor-intrinsic genomic properties discriminating response to CRT, we examined how transcriptional programs in the tumor or microenvironment were impacted by exposure to these therapies. Among the responders, there were 14 unique transcriptional programs significantly enriched in the pre-CRT samples and one unique transcriptional program significantly enriched in the post-CRT samples, with IFNα response genes enriched in both pre-/post-CRT samples (FWER P = 0.00; Fig. 3A). Among the NR, there were no unique significantly enriched transcriptional programs in the pre-CRT samples and there were five unique transcriptional programs significantly enriched in the post-CRT samples, with the angiogenesis and epithelial–mesenchymal transition (EMT) transcriptional programs enriched among both pre- and post-CRT samples (FWER P = 0.00; Fig. 3A).

Given the immune-related transcriptional programs enriched pre-/post-CRT, we next examined immune cell infiltrates inferred from bulk transcriptome data (Materials and Methods). Total immune infiltrate levels were significantly higher in post-CRT specimens relative to their pre-CRT counterparts (P = 0.04; Fig. 3B). Overall, we observed significantly more naïve B cells (P = 0.044), CD8 T cells (P = 0.002), monocytes (P = 0.01), M2 macrophages (P = 0.002), and resting mast cells (P = 0.0007) in the post-CRT tumor specimens. In contrast, there were significantly more memory B cells (P = 0.04) and activated mast cells (P = 0.006) in the pre-CRT tumor specimens (Supplementary Fig. S6).

Interestingly, when limiting the analysis to NR pre-/post-CRT, we observed significantly more M2 macrophages (P = 0.005; FDR q = 0.1) in the post-CRT tumor specimens, as well as naïve B cells (P = 0.03), monocytes (P = 0.03), and resting mast cells (P = 0.03), with significantly more activated mast cells in the pre-CRT specimens (P = 0.04; Fig. 3C).

To complement bulk transcriptome analysis, we also evaluated immune infiltrate using IHC for CD4 and CD8 T cells (Supplementary Table S4). The number of CD8 T cells trended toward a global increase between pre-CRT and post-CRT samples (P = 0.47; Supplementary Fig. S7). In the pre-CRT samples, there were more CD8 T cells in R compared with NR (P = 0.14; Fig. 4A) and complete responders (CR) samples had significantly more CD8 immune infiltration compared with NR (P = 0.04; Fig. 4A and B; Supplementary Figs. S8 and S9).

Globally, CD4 infiltrate decreased between pre- and post-CRT, but this trend was not statistically significant (P = 0.89; Supplementary Fig. S10). Similar to CD8 T cells, NR trended toward having less CD4 immune infiltration compared with R (P = 0.32; Fig. 4C). When further breaking down response into CR versus partial responders (PR), CR appeared to have more CD4 immune infiltrate compared with NR (P = 0.37; Fig. 4C and D; Supplementary Figs. S11 and S12). In summary, while IHC demonstrated significant differences in T-cell infiltrate pre-CRT between R versus NR, clear shifts in immune infiltrate composition were observed after CRT in NR patients based on bulk transcriptome analysis.

To our knowledge, this is the first study to evaluate both genomic and microenvironmental changes at a primary rectal cancer tumor site exposed to preoperative CRT. Our data provide an opportunity to understand treatment-associated genomic changes between pre- and post-CRT specimens directly in patients. Tumor evolution has been previously studied primarily in the context of systemic cancer therapeutics in solid tumors (23, 36, 58–61), while most radiotherapy-based studies have examined candidate germline features or leveraged microarray data (11, 12, 62–75). Here we performed integrative comprehensive molecular characterization to dissect tumor and immune properties that track with CRT resistance.

Tumor mutation burden has been extensively studied and is suggested to be a marker of tumor responsiveness to immune checkpoint blockade (18, 19). It has been hypothesized that radiation may be able to increase TMB through its DNA-damaging mechanisms. Our data did not demonstrate an increase in overall mutational or neoantigen load after exposure to CRT. This finding is consistent with other pre- and postmatched tumor evolutionary assessments in the context of systemic therapy, particularly with cisplatin-based chemotherapy (23). Our data support the notion that chemotherapy or radiation is generally insufficient to prime the immune system by creating appropriate mutations or neoantigens (76, 77).

While global genomic tumor properties were not clearly different between response groups, NR were more likely to harbor co-KRAS/TP53 mutations compared with R. The KP genotype has been previously suggested to be associated with radioresistance but the underlying mechanisms are poorly understood (55–57). Our observations in KP/NR cases suggest a previously unrecognized mechanism of immune suppression (Fig. 3). We demonstrated that NRs were more likely to express a M2 macrophage phenotype, as well as enrichment for an EMT transcriptional program in the post-CRT specimens. The M2 phenotype is known to be anti-inflammatory, proangiogenic, and metastasis promoting (78–80), while EMT plays a role in cancer metastasis and treatment resistance (81–85). Thus, KP/NR status may be associated with local immune escape during or after CRT. Of note, in our cohort without local recurrences, we found that NR/KP was associated with metastatic progression. Taken together, this suggests that KP/NR-associated local immune escape leads to distant metastatic disease and reduced PFS (Figs. 1B and 2D). Thus, these tumors may benefit from novel neoadjuvant treatment approaches to reduce the risk of immune escape and metastatic seeding.

There are several limitations to this study. Small patient numbers make additional in-depth analyses and conclusions difficult, hence our findings need validation in larger, independent cohorts in diverse clinical settings. Many of our associations may be dependent on one another, as we do not have enough events to appropriately determine whether KRAS/TP53 genotype or pCR rate is more predictive of PFS through a multivariable regression. We rely on pCR as a biomarker of response, which has been called into question after preoperative CRT for rectal cancer (86) as pCR can vary and may be a function of time between end of CRT and surgical resection, although it has been used as a robust endpoint when evaluating novel systemic agents in other solid tumors (4). Some of our findings may be attributable to samples having higher or lower initial tumor burden; to overcome this issue, we performed purity/ploidy corrected molecular analysis through the ABSOLUTE algorithm (35) to account for differences in stromally admixed tumor specimens. We did not evaluate the impact of short-course preoperative radiotherapy nor other high-dose ablative radiotherapy schedules, which may elicit more mutagenesis and an immune response within the tumor microenvironment due to the higher dose per fraction during treatment (16, 17, 87, 88). We also acknowledge that interpretation of in silico–derived neoantigens from the mutations for each sample requires significant validation for improved interpretation. In addition, tumor spatial heterogeneity cannot be ruled out in this study as we do not have data from multiple areas of each tissue sample.

Overall, our study creates a path forward by leveraging molecular profiling for consideration of preoperative CRT in patients with locally advanced tumors. This study also highlights the larger opportunity for additional investigations to elucidate novel mechanisms behind radioresistance across solid tumors.

R.B. Corcoran has ownership interests at Avidity Biosciences, nRichDx, and Fount Therapeutics, is a consultant/advisory board member for Array Biopharma, Astex Pharmaceuticals, Avidity Biosciences, Merrimack, Novartis, Shire, nRichDx, Bristol-Myers Squibb, FOG Pharma, Fount Therapeutics, N-of-One, Revolution Medicines, Taiho, Amgen, Chugai, Genentech, LOXO Oncology, Roivant, Roche, Spectrum Pharmaceuticals, Symphogen, and Warp Drive Bio, and reports receiving commercial research support from Asana, Sanofi, and AstraZeneca. T.S. Hong is a consultant/advisory board member for EMD Serono and Merck, and reports receiving commercial research support from Taiho, AstraZeneca, IntraOp, Bristol Myers Squibb and Ipsen. E.M. Van Allen has ownership interests (including patents) at Syapse, Microsoft, Genome Medical, and Tango Therapeutics, is a consultant/advisory board member for Genome Medical, Invitae, Tango Therapeutics, Dynamo, Illumina, and Foresite Capital, and reports receiving commercial research grants from Novartis and Bristol-Myers Squibb IION. No potential conflicts of interest were disclosed by the other authors.

The content is solely the responsibility of the authors and does not necessarily reflect the official views of the NIH.

Conception and design: S.C. Kamran, J.K. Lennerz, R.B. Corcoran, T.S. Hong, E.M. Van Allen

Development of methodology: S.C. Kamran, J.K. Lennerz, C.A. Margolis, D. Liu, B. Reardon, S.L. Carter, E.M. Van Allen

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): S.C. Kamran, J.K. Lennerz, E.E. Van Seventer, J.Y. Wo, T.S. Hong, E.M. Van Allen

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): S.C. Kamran, J.K. Lennerz, C.A. Margolis, D. Liu, B. Reardon, S.A. Wankowicz, J.Y. Wo, H. Willers, R.B. Corcoran, T.S. Hong, E.M. Van Allen

Writing, review, and/or revision of the manuscript: S.C. Kamran, J.K. Lennerz, C.A. Margolis, B. Reardon, S.A. Wankowicz, J.Y. Wo, H. Willers, R.B. Corcoran, T.S. Hong, E.M. Van Allen

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): S.C. Kamran, J.K. Lennerz, A. Tracy, E.M. Van Allen

Study supervision: S.C. Kamran, T.S. Hong, E.M. Van Allen

This work is funded by grants from Damon Runyon Foundation (to E.M. Van Allen), NCI R01CA227388, K08 CA188615, R37 CA222574, U01 CA233100 (E.M. Van Allen) NCI U01CA220714 (to H. Willers). This work was funded in part by NIH grant no. R01 CA225655 (to J.K. Lennerz).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.