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Genomic and transcriptomic determinants of response to neoadjuvant therapy in rectal cancer

Abstract

The incidence of rectal cancer is increasing in patients younger than 50 years. Locally advanced rectal cancer is still treated with neoadjuvant radiation, chemotherapy and surgery, but recent evidence suggests that patients with a complete response can avoid surgery permanently. To define correlates of response to neoadjuvant therapy, we analyzed genomic and transcriptomic profiles of 738 untreated rectal cancers. APC mutations were less frequent in the lower than in the middle and upper rectum, which could explain the more aggressive behavior of distal tumors. No somatic alterations had significant associations with response to neoadjuvant therapy in a treatment-agnostic manner, but KRAS mutations were associated with faster relapse in patients treated with neoadjuvant chemoradiation followed by consolidative chemotherapy. Overexpression of IGF2 and L1CAM was associated with decreased response to neoadjuvant therapy. RNA-sequencing estimates of immune infiltration identified a subset of microsatellite-stable immune hot tumors with increased response and prolonged disease-free survival.

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Fig. 1: The genomic landscape of rectal cancer.
Fig. 2: Differences in WNT signaling across the rectum.
Fig. 3: Clinical and genomic determinants of response to neoadjuvant therapy in LARC.
Fig. 4: Transcriptomic determinants of response to neoadjuvant therapy in LARC.
Fig. 5: Immune profiling identifies a subset of immune hot pMMR/MSS LARC tumors with favorable outcomes from neoadjuvant therapy.

Data availability

All genomic results and associated clinical data for all of the patients in this study have been deposited in the cBioPortal for Cancer Genomics55,56 and are publicly available for browsing and bulk download at https://www.cbioportal.org/study/summary?id=rectal_msk_2022. The raw RNA sequencing data have also been deposited in GEO (accession number GSE209746 available at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE209746). The raw DNA sequencing data are protected; de-identified data are available under restricted access to protect patient privacy in accordance with Federal and State law. These data can be requested for research use from the corresponding author. Data will be shared for a span of 2 years within 2 weeks of execution of a data transfer agreement with MSK, which will retain all title and rights to the data and results from their use. The OncoKB knowledge base that we used to annotate genomic alterations is publicly available at https://www.oncokb.org/. TCGA data used for comparison are available via the Genomic Data Commons Portal (https://portal.gdc.cancer.gov/).

Code availability

The mutational signature decomposition code can be found at https://github.com/mskcc/tempoSig. The OncoKB annotator tool is also available through its own GitHub repository at https://github.com/oncokb. Additional custom written tools and programs used for the analysis of MSK-IMPACT data are available through the MSK GitHub repository at https://github.com/mskcc.

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Acknowledgements

The authors acknowledge the use of services provided by the Molecular Cytology Core Facility, funded by the National Cancer Institute (NCI) Cancer Center Support Grant (CCSG, P30 CA008748-53). The authors also acknowledge the use of the Integrated Genomics Operation Core, funded by the NCI Cancer Center Support Grant (CCSG, P30 CA08748), Cycle for Survival, and the Marie-Josée and Henry R. Kravis Center for Molecular Oncology. W.K.C. is supported by a National Institutes of Health (NIH) research training grant (T32 GM132083). P.B.R. is supported by an NIH/NCI early career development award (K08 CA255574). J.J.S. is supported by an NIH/NCI R37 248289 award. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Author information

Authors and Affiliations

Authors

Contributions

W.K.C., F.S.-V. and J.G.-A. conceived and designed the study in concert with N.S., R.Y. and J.J.S. A.C., M.R.W., M.W., I.H.W., E.P.P., G.M.N., P.B.P., Q.S., K.G., R.Y., J.J.S. and J.G.-A. provided patient samples and clinical information. C.-T.C., S.-H.C., M.R.M., J.K.K., O.S.C. and M.F.B. generated genomic data. J.K.K., M.R.M., C.-T.C., D.M.O., K.I.G. and J.G.-A. annotated patient samples and curated the clinical data. W.K.C., F.S.-V., H.W., K.G., X.Q., F.W., P.B.R. and J.G.-A. performed data analysis and interpretation. J.S. and E.V. carried out the histopathologic analyses. D.N.K. interpreted the immunology results. S.D.S. carried out the project management. W.K.C., H.W., F.W., X.Q., R.P., R.K., M.T.A.D., Y.-J.H., A.L., M.F.B., F.S.V. and N.S., provided bioinformatic support for data analysis, annotation, visualization and dissemination. M.F.B., D.B.S. and N.S. provided support as part of the institutional MSK-IMPACT sequencing initiative. W.K.C., H.W., R.Y., J.J.S., N.S., F.S.-V. and J.G.-A. wrote the manuscript. All authors reviewed and approved the manuscript. N.S., R.Y., F.S.-V. and J.G.-A. provided resources and funding for this work. F.S.-V. and J.G.-A. supervised the study.

Corresponding authors

Correspondence to Francisco Sanchez-Vega or Julio Garcia-Aguilar.

Ethics declarations

Competing interests

D.N.K. has been a consultant for Merck Sharp & Dohme with regard to intellectual property rights and for AbbVie and PsiOxus Therapeutics Ltd with regard to provision of services. E.P.P. has received support from Intuitive Surgical. Q.S. reports a consulting or advisory role with Yiviva, Boehringer Ingelheim Pharmaceuticals, Regeneron Pharmaceuticals, Hoosier Cancer Research Network (to self), an honorarium or speaker role with Chugai Pharmaceutical Co., stocks from Johnson & Johnson, Amgen and Merck & CO. (to self), and research funds from Celgene/BMS, Roche/Genentech, Janssen and Novartis (to institution). D.B.S. has consulted for and received honoraria from Pfizer, Lilly/Loxo Oncology, Vividion Therapeutics, Scorpion Therapeutics and BridgeBio. M.F.B. has consulted for Eli Lilly and PetDx, and has received research funding from Grail not related to the work presented. P.B.R. is an EMD Serono consultant and reports support for travel from Elekta and Philips healthcare and prior research funding from EMD Serono. R.Y. has been an advisor for Pfizer, Mirati Therapeutics and Natera, and has received research support from Pfizer, Boehringer Ingelheim and Forte Biosciences. J.J.S. has received travel support from Intuitive Surgical for fellow education and has served as a clinical advisor for Guardant Health. J.G.-A. has received an honorarium for being a consultant with Medtronics, Ethicon, Johnson & Johnson and Intuitive Surgical, and owns stock in Intuitive Surgical. All other authors have no competing interests.

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Extended data

Extended Data Fig. 1 Cohort overview and patient breakdown by analyses.

((a) Overview of the different sample sets used for the different analyses described in the manuscript, including sample sizes and reasons for exclusion. (b) Venn diagrams showing overlaps for patients with available MSK-IMPACT, WES, RNA-Seq and neoadjuvant therapy (NAT) outcome data. Color bars show the distribution of different relevant clinical variables. (c-h) Same as B, but restricted to the subset of patients used in specific analyses described in the manuscript. Thick red contours drawn on top of the Venn diagrams are used to highlight the set of patients used in each case. The G# in the titles refer to the columns found in Supplementary Table 1.

Extended Data Fig. 2 Additional insights into the genomic landscape of rectal cancer.

(a) Overview of driver alterations in rectal cancer stratified by tumor stage. (b) Distribution of clonal versus driver mutations for the most frequently mutated genes in our rectal cancer cohort. (c) Fraction of samples with two driver mutations in selected genes where both are clonal, both are subclonal or only one is clonal. (d) Distribution of KRAS mutations stratified by affected codon and specific amino acid change. Blue vertical bars show the fraction of clonal versus subclonal mutations. Red and gray bars show the fraction of samples with allelic imbalance (mutant selection). (e) Distribution of mutational signatures for samples in the WES cohort. Samples were ordered from left to right in terms of decreasing SBS1 signature (mitotic clock) and stratified according to dMMR/MSI status.

Extended Data Fig. 3 Comparison of colon and rectal adenocarcinomas.

(a) Clinicopathological features for right colon, left colon, and rectum samples. (b) Differences in first site of metastasis stratified by primary tumor location. (c) Tumor mutational burden (TMB) and FGA in pMMR/MSS tumors from the right colon (n = 121), left colon (n = 187), and rectum (n = 449). Statistical significance was assessed using a two-sided Mann–Whitney U-test. (d) Frequency of somatic alterations in oncogenic signaling pathways by anatomic location. Significant results were denoted as * indicating q < 0.05, ** indicating q < 0.01, *** indicating q < 0.005, and **** indicating q < 0.001. (e) Frequency of RAS/RAF alterations in hypermutated and non-hypermutated tumors stratified by tumor location. (f) Copy number profiles for tumors in the analyzed cohorts. (g) Frequency of copy number alterations affecting the p and q arms of chromosome 20 by anatomic location. (h) FGA as a function of TP53 status, stratified by missense versus truncating and mono-allelic versus biallelic inactivation, for tumors from the right colon (wild-type n = 39, missense n = 8, missense biallelic n = 33, truncating n = 1, truncating biallelic n = 17), left colon (wild-type n = 32, missense n = 10, missense biallelic n = 77, truncating n = 5, truncating biallelic n = 29) and rectum (wild-type n = 73, missense n = 44, missense biallelic n = 175, truncating n = 12, truncating biallelic n = 81). (i) Fraction of dMMR/MSI tumors by rectal segment. (j) Distance to the anal verge by APC status in the validation cohort of metastatic patients. APC WT (n = 43) were compared to APC altered (n = 115) using a two-sided Mann–Whitney U-test, * indicates p = 0.0029. (k) Distribution of APC mutations by genomic location in tumors from the right colon, left colon, upper rectum, middle rectum, and lower rectum. In panels (B), (D) and (G), statistical significance was assessed using a two-sided Fisher’s exact test and p values were corrected for multiple testing using false discovery rate. In panels (C), (H) and (J), boxplots’ center lines indicate medians, edges indicate the interquartile range, and whiskers extend to the highest and lowest values not considered outliers.

Extended Data Fig. 4 Clinical and genomic determinants of response to NAT in LARC.

(a) Frequency of somatic alterations in rectal cancer driver genes for the patients used in our analyses of clinical outcomes, stratified by cohort. (b) Frequency of somatic alterations in oncogenic signaling pathways for the patients used in our analyses of clinical outcomes, stratified by cohort. (c) Left panel shows results from a multivariate analysis of associations between CR and a combination of clinicopathological and genomic features using a logistic regression model. The error bars indicate the 95% confidence interval. Right panel shows results from a multivariate analysis of associations between DFS and a combination of clinicopathological and genomic features using a Cox proportional hazards model. The results shown in this panel were obtained using patients treated with CRT-CNCT. (d) The left panel shows a multivariate analysis of associations between CR and a combination of clinicopathological and genomic features using a logistic regression model. The error bars indicate the 95% confidence interval. The right panel shows results from a multivariate analysis of associations between DFS and a combination of clinicopathological and genomic features using a Cox proportional hazards model. The results shown in this panel were obtained using patients treated with INCT-CRT.

Extended Data Fig. 5 Stratification of rectal adenocarcinomas using the consensus molecular subtypes (CMS) classification.

(a) Expression levels for selected genes stratified by CMS group. Genes were annotated using the signatures from Budinska et al.70. (b) TMB stratified by CMS groups. Sample sizes are: CMS1 (n = 11), CMS2 (n = 26), CMS3 (n = 26), and CMS4 (n = 38). (c) FGA stratified by CMS groups. Sample sizes are: CMS1 (n = 11), CMS2 (n = 26), CMS3 (n = 26), and CMS4 (n = 38). (d) Percentage of KRAS mutated tumors by CMS group. (e) ssGSEA scores for selected pathways from the Hallmark dataset35. Sample sizes are: CMS1 (n = 11), CMS2 (n = 26), CMS3 (n = 26), and CMS4 (n = 38). (f) DFS for LARC patients treated with NAT, stratified by CMS group. (g) Levels of CA9 gene expression as a function of KRAS and PIK3CA mutational status. Double mutants and KRAS-mutant tumors had significantly higher expression of CA9 compared to wild-type tumors, p = 1.3e-07 and p = 4.65e-05, respectively. Sample sizes are: Double-mutant (n = 8), KRAS-mutant (n = 26), PIK3CA-mutant (n = 6), and wild-type (n = 5). Statistical significance was assessed using a two-sided Mann–Whitney U-test. (h) Expression of L1CAM stratified by CMS group. L1CAM expression was higher in CMS2 and CMS4 compared to CMS3, q = 0.0498 and q = 0.096, respectively. Sample sizes are: CMS1 (n = 11), CMS2 (n = 26), CMS3 (n = 26), and CMS4 (n = 38). (i) Validation of transcriptomic findings using an independent cohort of 15 LARC cases from Kamran et al.10 Differential gene expression was conducted using DESeq2 and the p-values attained by the Wald test were corrected using false discovery rates. In panels (B), (C), (E) and (H), statistical significance was assessed using a two-sided Mann–Whitney U-test. P values were corrected using the Bonferroni method and significant results are denoted as *q < 0.05, **q < 0.01, ***q < 0.005 and ****q < 0.001. In panels (B), (C), (E), (G), and (H), boxplots’ center lines indicate medians, edges indicate the interquartile range, and the whiskers extend to the highest and lowest values not considered outliers.

Extended Data Fig. 6 Supporting information for the characterization of immune hot pMMR/MSS LARC tumors with favorable outcomes from NAT.

(a) Quantification of intra-tumoral TILs from H&E slides for 20 patients, including cases from IG1 (n = 6), IG2 (n = 6), IG3 (n = 5) and IG4 (n = 3). Statistical significance was assessed using a two-sided Mann–Whitney U-test. P values were corrected using the Bonferroni method. Boxplots’ center lines indicate medians, edges indicate the interquartile range, and the whiskers extend to the highest and lowest values not considered outliers. Right panel shows correlation between estimated fractions of intra-tumoral and inter-tumoral TILs. Statistical significance was assessed using a two-sided Spearman correlation. Error bands represent 95% confidence intervals. (b) ssGSEA scores for immune cell signatures from Bindea et al.32. Displayed cell types are the ones with an adjusted p-value < 0.10 after Bonferroni correction, based on a Kruskal-Wallis test. (c) Comparison of ssGSEA scores for specific oncogenic pathway signatures from the Hallmark set35 across the four immune clusters. Displayed cell types are the ones with an adjusted p-value < 0.10 after Bonferroni correction, based on a Kruskal-Wallis test. In panels (B) and (C), sample sizes are: IG1 (n = 52), IG2 (n = 37), IG3 (n = 7), and IG4 (n = 5). (d) Correlation plot showing gene signatures for 27 selected oncogenic pathways (yellow diamonds) and immune cell infiltrates (green diamonds). Right panels show illustrative scatter plots for pairs of variables with strong positive and negative correlations. White dots in the correlation heatmap highlight pairs of variables with significant two-sided Spearman correlation after Bonferroni correction. Error bands represent 95% confidence intervals. In panels (B) and (C), statistical significance was assessed using a two-sided Mann–Whitney U-test. P values were corrected using the Bonferroni method and significant results are denoted as *q < 0.05, **q < 0.01, ***q < 0.005 and ****q < 0.001. Boxplots’ center lines indicate medians, edges indicate the interquartile range, and the whiskers extend to the highest and lowest values not considered outliers.

Extended Data Fig. 7 Validation of immune groups in an independent cohort of LARC tumors from TCGA.

Validation of results using an independent cohort of 42 LARC samples from TCGA. (a) Unsupervised hierarchical clustering of pMMR/MSS tumors using ssGSEA scores for a set of well established immune signatures reveals three groups with increasing levels of overall immune infiltrate (IG1–IG3). dMMR/MSI tumors were added later as a fourth group (IG4). (b) Tumors in IG4 had higher TMB and had lower FGA than tumors in the IG1–IG3 groups. Sample sizes for each group are as follows: IG1 (n = 16), IG2 (n = 17), IG3 (n = 7), and IG4 (n = 2). Boxplots’ center lines indicate medians, edges indicate the interquartile range, and the whiskers extend to the highest and lowest values not considered outliers. (c) Distribution of CMS classes across immune groups. (d) Selected significant differences in ssGSEA scores for specific immune cell types across immune groups. Sample sizes for each group are as follows: IG1 (n = 16), IG2 (n = 17), IG3 (n = 7), and IG4 (n = 2). (e) Comparison of expression levels for genes encoding proteins involved in immune checkpoint blockade. Sample sizes for each group are as follows: IG1 (n = 16), IG2 (n = 17), IG3 (n = 7), and IG4 (n = 2). In panels (D) and (E), statistical significance was assessed using a two-sided Mann–Whitney U-test. P values were corrected using the Bonferroni method and significant results are denoted as *q < 0.05, **q < 0.01, ***q < 0.005 and ****q < 0.001. Boxplots’ center lines indicate medians, edges indicate the interquartile range, and the whiskers extend to the highest and lowest values not considered outliers.

Supplementary information

Reporting Summary

Supplementary Tables

Supplementary Tables 1–11: Table S1, Clinical, histopathological, and sequencing data; Table S2: Summary of clinical characteristics for the full cohort. Table S3: Overview of cohorts and key clinicopathological features. Table S4: Summary of MutSigCV analysis using WES samples. Table S5: Sample identifiers and clinical information for cases in supplemental cohorts. Table S6: Summary of clinical characteristics for the treatment response cohort. Table S7: Summary of outcome analysis (response and DFS) using genomic data. Table S8: Summary of outcome analysis (response and DFS) using transcriptomic data. . Table S9: Results from TIL quantification analyses. Table S10: Validation of immune profiling results using data from TCGA. Table S11: List of genes on the MSK-IMPACT targeted sequencing panels.

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Chatila, W.K., Kim, J.K., Walch, H. et al. Genomic and transcriptomic determinants of response to neoadjuvant therapy in rectal cancer. Nat Med 28, 1646–1655 (2022). https://doi.org/10.1038/s41591-022-01930-z

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